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Application of Genetic Algorithm for More Efficient Multi-Layer Thickness Optimization in Solar Cells

Author

Listed:
  • Premkumar Vincent

    (School of Electronics Engineering, Kyungpook National University, 80 Daehakro, Bukgu, Daegu 41566, Korea
    Co-first authors with equal contribution.)

  • Gwenaelle Cunha Sergio

    (School of Electronics Engineering, Kyungpook National University, 80 Daehakro, Bukgu, Daegu 41566, Korea
    Co-first authors with equal contribution.)

  • Jaewon Jang

    (School of Electronics Engineering, Kyungpook National University, 80 Daehakro, Bukgu, Daegu 41566, Korea)

  • In Man Kang

    (School of Electronics Engineering, Kyungpook National University, 80 Daehakro, Bukgu, Daegu 41566, Korea)

  • Jaehoon Park

    (College of Software, Hallym University, Chuncheon 24252, Korea)

  • Hyeok Kim

    (Department of Electrical and Computer Engineering, University of Seoul, 163 Seoulsiripdaero, Dongdaemun-gu, Seoul 02504, Korea)

  • Minho Lee

    (Department of Artificial Intelligence, Kyungpook National University, 80 Daehakro, Bukgu, Daegu 41566, Korea)

  • Jin-Hyuk Bae

    (School of Electronics Engineering, Kyungpook National University, 80 Daehakro, Bukgu, Daegu 41566, Korea)

Abstract

Thin-film solar cells are predominately designed similar to a stacked structure. Optimizing the layer thicknesses in this stack structure is crucial to extract the best efficiency of the solar cell. The commonplace method used in optimization simulations, such as for optimizing the optical spacer layers’ thicknesses, is the parameter sweep. Our simulation study shows that the implementation of a meta-heuristic method like the genetic algorithm results in a significantly faster and accurate search method when compared to the brute-force parameter sweep method in both single and multi-layer optimization. While other sweep methods can also outperform the brute-force method, they do not consistently exhibit 100% accuracy in the optimized results like our genetic algorithm. We have used a well-studied P3HT-based structure to test our algorithm. Our best-case scenario was observed to use 60.84% fewer simulations than the brute-force method.

Suggested Citation

  • Premkumar Vincent & Gwenaelle Cunha Sergio & Jaewon Jang & In Man Kang & Jaehoon Park & Hyeok Kim & Minho Lee & Jin-Hyuk Bae, 2020. "Application of Genetic Algorithm for More Efficient Multi-Layer Thickness Optimization in Solar Cells," Energies, MDPI, vol. 13(7), pages 1-13, April.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:7:p:1726-:d:341572
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